SWOT Simulated North American Continent Hydrology Dataset Exploration in the Cloud

From the PO.DAAC Cookbook, to access the GitHub version of the notebook, follow this link.

Accessing and Visualizing SWOT Simulated Datasets

Requirement:

This tutorial can only be run in an AWS cloud instance running in us-west-2: NASA Earthdata Cloud data in S3 can be directly accessed via earthaccess python library; this access is limited to requests made within the US West (Oregon) (code: us-west-2) AWS region.

Learning Objectives:

• Access all 6 products of SWOT HR sample data (archived in NASA Earthdata Cloud) within the AWS cloud, without downloading to local machine
• Visualize accessed data

SWOT Simulated Level 2 North America Continent KaRIn High Rate Version 1 Datasets:

1. River Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1

DOI: https://doi.org/10.5067/KARIN-2RSP1

2. Lake Vector Shapefile - SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1

DOI: https://doi.org/10.5067/KARIN-2LSP1

3. Water Mask Pixel Cloud NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1

DOI: https://doi.org/10.5067/KARIN-2PIX1

4. Water Mask Pixel Cloud Vector Attribute NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1

DOI: https://doi.org/10.5067/KARIN-2PXV1

5. Raster NetCDF - SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1

DOI: https://doi.org/10.5067/KARIN-2RAS1

6. KaRIn Sea Surface Height from GLORYSs - SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1

DOI: https://doi.org/10.5067/KARIN-2GLC1

Notebook Authors: Cassie Nickles and Zoë Walschots, NASA PO.DAAC (July 2023)

Libraries Needed

import glob
import os
import requests
import s3fs
import netCDF4 as nc
from netCDF4 import Dataset
import h5netcdf
import xarray as xr
import pandas as pd
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt
import hvplot.xarray
import cartopy.crs as ccrs
import cartopy.feature as cfeat
import shapefile as shp
import zipfile
import earthaccess
from earthaccess import Auth, DataCollections, DataGranules, Store

Earthdata Login

An Earthdata Login account is required to access data, as well as discover restricted data, from the NASA Earthdata system. Thus, to access NASA data, you need Earthdata Login. Please visit https://urs.earthdata.nasa.gov to register and manage your Earthdata Login account. This account is free to create and only takes a moment to set up. We use earthaccess to authenticate your login credentials below.

auth = earthaccess.login(strategy="interactive", persist=True)

Enter your Earthdata Login username:  zoewalschots
Enter your Earthdata password:  ········

You're now authenticated with NASA Earthdata Login
Using token with expiration date: 08/12/2023
Using user provided credentials for EDL
Persisting credentials to .netrc

Set up an s3fs session for Direct Access

s3fs sessions are used for authenticated access to s3 bucket and allows for typical file-system style operations. Below we create session by passing in the data access information.

fs_s3 = earthaccess.get_s3fs_session(daac='PODAAC', provider='POCLOUD')

Single File Access

The s3 access link can be found using earthaccess data search. Since this collection consists of Reach and Node files, we need to extract only the granule for the Reach file. We do this by filtering for the ‘Reach’ title in the data link.

Alternatively, Earthdata Search (see tutorial) can be used to manually search for a single file.

1. River Vector Shapefiles

#retrieves granule from the day we want
river_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_RIVERSP_V1', temporal = ('2022-08-22 19:24:41', '2022-08-22 19:30:37'))

#finds the s3 link of the one granule we want (The collection contains both Reaches and Nodes, but here we want only the Reach)
river_data = []
for g in river_results:
    for l in earthaccess.results.DataGranule.data_links(g, access='direct'):
        if "Reach" in l:
            river_data.append(l)
print(river_data)

#creates s3 file object for the one link we want
s3_file_obj1 = fs_s3.open(river_data[0], mode='rb')

The native format for this sample data is a .zip file, and we want the .shp file within the .zip file, so we need to download the contents of the zip file into the cloud environment. I created a folder called SWOT_HR_shp to write to. Change the path to where you would like your extracted files to be written.

with zipfile.ZipFile(s3_file_obj1, 'r') as zip_ref:
    zip_ref.extractall('SWOT_HR_shp')

Next, we’ll look at the attribute table of the .shp file we just extracted to the ‘SWOT_HR_shp’ folder.

SWOT_HR_shp1 = gpd.read_file('SWOT_HR_shp/SWOT_L2_HR_RiverSP_Reach_007_522_NA_20220822T192441_20220822T193037_PGA0_01.shp') 
SWOT_HR_shp1

fig, ax = plt.subplots(figsize=(11,7))
SWOT_HR_shp1.plot(ax=ax, color='black')

2. Lake Vector Shapefiles

The lake vector shapefiles can be accessed in the same way as the river shapefiles above.

lake_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_LAKESP_V1', temporal = ('2022-08-22 19:24:18', '2022-08-22 19:30:50'))

#find the s3 link of the desired granule (This collection has three options: Obs, Unassigned, and Prior - we want Obs)
lake_data = []
for g in lake_results:
    for l in earthaccess.results.DataGranule.data_links(g, access='direct'):
        if "Obs" in l:
            lake_data.append(l)
print(lake_data)

s3_file_obj2 = fs_s3.open(lake_data[0], mode='rb')

with zipfile.ZipFile(s3_file_obj2, 'r') as zip_ref:
    zip_ref.extractall('SWOT_HR_shp')

SWOT_HR_shp2 = gpd.read_file('SWOT_HR_shp/SWOT_L2_HR_LakeSP_Obs_007_522_NA_20220822T192415_20220822T193051_Dx0000_01.shp') 
SWOT_HR_shp2

fig, ax = plt.subplots(figsize=(7,12))
SWOT_HR_shp2.plot(ax=ax, color='black')

3. Water Mask Pixel Cloud NetCDF

Accessing the remaining files is different than the shp files above. We do not need to unzip the files because they are stored in native netCDF files in the cloud. For the rest of the products, we will open via xarray.

watermask_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXC_V1', temporal = ('2022-08-22 19:29:00', '2022-08-22 19:29:11'), point = ('-90', '35'))

The pixel cloud netCDF files are formatted with three groups titled, “pixel cloud”, “tvp”, or “noise” (more detail here). In order to access the coordinates and variables within the file, a group must be specified when calling xarray open_dataset.

ds_PIXC = xr.open_mfdataset(earthaccess.open([watermask_results[0]]), group = 'pixel_cloud', engine='h5netcdf')
ds_PIXC

plt.scatter(x=ds_PIXC.longitude, y=ds_PIXC.latitude, c=ds_PIXC.height)
plt.colorbar().set_label('Height (m)')

4. Water Mask Pixel Cloud Vector Attribute NetCDF

vector_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_PIXCVEC_V1', temporal = ('2022-08-22 19:29:00', '2022-08-22 19:29:11'), point = ('-90', '35'))

ds_PIXCVEC = xr.open_mfdataset(earthaccess.open([vector_results[0]]), decode_cf=False,  engine='h5netcdf')
ds_PIXCVEC

pixcvec_htvals = ds_PIXCVEC.height_vectorproc.compute()
pixcvec_latvals = ds_PIXCVEC.latitude_vectorproc.compute()
pixcvec_lonvals = ds_PIXCVEC.longitude_vectorproc.compute()

#Before plotting, we set all fill values to nan so that the graph shows up better spatially
pixcvec_htvals[pixcvec_htvals > 15000] = np.nan
pixcvec_latvals[pixcvec_latvals > 80] = np.nan
pixcvec_lonvals[pixcvec_lonvals > 180] = np.nan

plt.scatter(x=pixcvec_lonvals, y=pixcvec_latvals, c=pixcvec_htvals)
plt.colorbar().set_label('Height (m)')

5. Raster NetCDF

raster_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_NA_CONTINENT_L2_HR_RASTER_V1', temporal = ('2022-08-22 19:28:50', '2022-08-22 19:29:11'), point = ('-90', '35'))

#this collection has 100m and 250m granules, but we only want 100m
raster_data = []
for g in raster_results:
    for l in earthaccess.results.DataGranule.data_links(g, access='direct'):
        if "100m" in l:
            raster_data.append(l)
print(raster_data)

ds_raster = xr.open_mfdataset(earthaccess.open([raster_data[0]], provider = 'POCLOUD'), engine='h5netcdf')
ds_raster

It’s easy to analyze and plot the data with packages such as hvplot!

ds_raster.wse.hvplot.image(y='y', x='x')

Multi-File

glorys_results = earthaccess.search_data(short_name = 'SWOT_SIMULATED_L2_KARIN_SSH_GLORYS_CALVAL_V1', temporal = ('2015-05-01 00:00:00', '2015-05-01 23:59:59'))

Granules found: 29

ds = xr.open_mfdataset(earthaccess.open(glorys_results), combine='nested', concat_dim="num_lines", decode_times=False, engine='h5netcdf')
ds

 Opening 29 granules, approx size: 0.0 GB

<xarray.Dataset>
Dimensions:                                (num_lines: 286144, num_pixels: 71,
                                            num_sides: 2)
Coordinates:
    latitude                               (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
    longitude                              (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
    latitude_nadir                         (num_lines) float64 dask.array<chunksize=(9868,), meta=np.ndarray>
    longitude_nadir                        (num_lines) float64 dask.array<chunksize=(9868,), meta=np.ndarray>
Dimensions without coordinates: num_lines, num_pixels, num_sides
Data variables: (12/91)
    time                                   (num_lines) float64 dask.array<chunksize=(9868,), meta=np.ndarray>
    time_tai                               (num_lines) float64 dask.array<chunksize=(9868,), meta=np.ndarray>
    ssh_karin                              (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
    ssh_karin_uncert                       (num_lines, num_pixels) float32 dask.array<chunksize=(9868, 71), meta=np.ndarray>
    ssha_karin                             (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
    ssh_karin_2                            (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
    ...                                     ...
    simulated_error_karin                  (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
    simulated_error_timing                 (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
    simulated_error_baseline_dilation      (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
    simulated_error_roll                   (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
    simulated_error_phase                  (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
    simulated_error_orbital                (num_lines, num_pixels) float64 dask.array<chunksize=(9868, 71), meta=np.ndarray>
Attributes: (12/32)
    Conventions:                CF-1.7
    title:                      Level 2 Low Rate Sea Surface Height Data Prod...
    institution:                CNES/JPL
    source:                     Simulate product
    history:                    2021-09-10 10:37:14Z : Creation
    platform:                   SWOT
    ...                         ...
    right_last_longitude:       157.4108182844071
    right_last_latitude:        77.03307806541929
    wavelength:                 0.008385803020979
    orbit_solution:             POE
    ellipsoid_semi_major_axis:  6378137.0
    ellipsoid_flattening:       0.003352810664781205

xarray.Dataset

• Dimensions:
  • num_lines: 286144
  • num_pixels: 71
  • num_sides: 2
• Coordinates: (4)
  • latitude
    (num_lines, num_pixels)
    float64
    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    long_name :

    latitude (positive N, negative S)

    standard_name :

    latitude

    units :

    degrees_north

    valid_min :

    -80000000

    valid_max :

    80000000

    comment :

    Latitude of measurement [-80,80]. Positive latitude is North latitude, negative latitude is South latitude.

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    longitude

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    longitude (degrees East)

    standard_name :

    longitude

    units :

    degrees_east

    valid_min :

    0

    valid_max :

    359999999

    comment :

    Longitude of measurement. East longitude relative to Greenwich meridian.

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    latitude_nadir

    (num_lines)

    float64

    dask.array<chunksize=(9868,), meta=np.ndarray>

    

    long_name :

    latitude of satellite nadir point

    standard_name :

    latitude

    units :

    degrees_north

    valid_min :

    -80000000

    valid_max :

    80000000

    comment :

    Geodetic latitude [-80,80] (degrees north of equator) of the satellite nadir point.

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144,) (9868,) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    longitude_nadir

    (num_lines)

    float64

    dask.array<chunksize=(9868,), meta=np.ndarray>

    

    long_name :

    longitude of satellite nadir point

    standard_name :

    longitude

    units :

    degrees_east

    valid_min :

    0

    valid_max :

    359999999

    comment :

    Longitude (degrees east of Grenwich meridian) of the satellite nadir point.

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144,) (9868,) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    Data variables: (91)

    time

    (num_lines)

    float64

    dask.array<chunksize=(9868,), meta=np.ndarray>

    

    long_name :

    time in UTC

    standard_name :

    time

    calendar :

    gregorian

    leap_second :

    YYYY-MM-DDThh:mm:ssZ

    units :

    seconds since 2000-01-01 00:00:00.0

    comment :

    Time of measurement in seconds in the UTC time scale since 1 Jan 2000 00:00:00 UTC. [tai_utc_difference] is the difference between TAI and UTC reference time (seconds) for the first measurement of the data set. If a leap second occurs within the data set, the attribute leap_second is set to the UTC time at which the leap second occurs.

    tai_utc_difference :

    35.0

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144,) (9868,) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    time_tai

    (num_lines)

    float64

    dask.array<chunksize=(9868,), meta=np.ndarray>

    

    long_name :

    time in TAI

    standard_name :

    time

    calendar :

    gregorian

    tai_utc_difference :

    [Value of TAI-UTC at time of first record]

    units :

    seconds since 2000-01-01 00:00:00.0

    comment :

    Time of measurement in seconds in the TAI time scale since 1 Jan 2000 00:00:00 TAI. This time scale contains no leap seconds. The difference (in seconds) with time in UTC is given by the attribute [time:tai_utc_difference].

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144,) (9868,) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    ssh_karin

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    sea surface height

    standard_name :

    sea surface height above reference ellipsoid

    units :

    m

    valid_min :

    -15000000

    valid_max :

    150000000

    comment :

    Fully corrected sea surface height measured by KaRIn. The height is relative to the reference ellipsoid defined in the global attributes. This value is computed using radiometer measurements for wet troposphere effects on the KaRIn measurement (e.g., rad_wet_tropo_cor and sea_state_bias_cor).

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    ssh_karin_uncert

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    sea surface height anomaly uncertainty

    units :

    m

    valid_min :

    0

    valid_max :

    60000

    comment :

    1-sigma uncertainty on the sea surface height from the KaRIn measurement.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    ssha_karin

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    sea surface height anomaly

    units :

    m

    valid_min :

    -1000000

    valid_max :

    1000000

    comment :

    Sea surface height anomaly from the KaRIn measurement = ssh_karin - mean_sea_surface_cnescls - solid_earth_tide - ocean_tide_fes – internal_tide_hret - pole_tide - dac.

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    ssh_karin_2

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    sea surface height

    standard_name :

    sea surface height above reference ellipsoid

    units :

    m

    valid_min :

    -15000000

    valid_max :

    150000000

    comment :

    Fully corrected sea surface height measured by KaRIn. The height is relative to the reference ellipsoid defined in the global attributes. This value is computed using model-based estimates for wet troposphere effects on the KaRIn measurement (e.g., model_wet_tropo_cor and sea_state_bias_cor_2).

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    ssha_karin_2

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    sea surface height anomaly

    units :

    m

    valid_min :

    -1000000

    valid_max :

    1000000

    comment :

    Sea surface height anomaly from the KaRIn measurement = ssh_karin_2 - mean_sea_surface_cnescls - solid_earth_tide - ocean_tide_fes – internal_tide_hret - pole_tide - dac.

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    ssha_karin_qual

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    sea surface height quality flag

    standard_name :

    status_flag

    flag_meanings :

    good bad

    flag_values :

    [0 1]

    valid_min :

    0

    valid_max :

    1

    comment :

    Quality flag for the SSHA from KaRIn.

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    polarization_karin

    (num_lines, num_sides)

    object

    dask.array<chunksize=(9868, 2), meta=np.ndarray>

    

    long_name :

    polarization for each side of the KaRIn swath

    comment :

    H denotes co-polarized linear horizontal, V denotes co-polarized linear vertical.

    Array Chunk Bytes 4.37 MiB 154.19 kiB Shape (286144, 2) (9868, 2) Dask graph 29 chunks in 59 graph layers Data type object numpy.ndarray

    swh_karin

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    significant wave height from KaRIn

    standard_name :

    sea_surface_wave_significant_height

    units :

    m

    valid_min :

    0

    valid_max :

    25000

    comment :

    Significant wave height from KaRIn volumetric correlation.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    swh_karin_uncert

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    1-sigma uncertainty on significant wave height from KaRIn

    units :

    m

    valid_min :

    0

    valid_max :

    25000

    comment :

    1-sigma uncertainty on significant wave height from KaRIn.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    sig0_karin

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    normalized radar cross section (sigma0) from KaRIn

    standard_name :

    surface_backwards_scattering_coefficient_of_radar_wave

    units :

    1

    valid_min :

    -1000.0

    valid_max :

    10000000.0

    comment :

    Normalized radar cross section (sigma0) from KaRIn in real, linear units (not decibels). The value may be negative due to noise subtraction. The value is corrected for instrument calibration and atmospheric attenuation. Radiometer measurements provide the atmospheric attenuation (sig0_cor_atmos_rad).

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    sig0_karin_uncert

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    1-sigma uncertainty on sigma0 from KaRIn

    units :

    1

    valid_min :

    0.0

    valid_max :

    1000.0

    comment :

    1-sigma uncertainty on sigma0 from KaRIn.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    sig0_karin_2

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    normalized radar cross section (sigma0) from KaRIn

    standard_name :

    surface_backwards_scattering_coefficient_of_radar_wave

    units :

    1

    valid_min :

    -1000.0

    valid_max :

    10000000.0

    comment :

    Normalized radar cross section (sigma0) from KaRIn in real, linear units (not decibels). The value may be negative due to noise subtraction. The value is corrected for instrument calibration and atmospheric attenuation. A meteorological model provides the atmospheric attenuation (sig0_cor_atmos_model).

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    wind_speed_karin

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    wind speed from KaRIn

    standard_name :

    wind_speed

    source :

    TBD

    units :

    m/s

    valid_min :

    0

    valid_max :

    65000

    comment :

    Wind speed from KaRIn computed from sig0_karin.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    wind_speed_karin_2

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    wind speed from KaRIn

    standard_name :

    wind_speed

    source :

    TBD

    units :

    m/s

    valid_min :

    0

    valid_max :

    65000

    comment :

    Wind speed from KaRIn computed from sig0_karin_2.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    swh_karin_qual

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    quality flag for significant wave height from KaRIn.

    standard_name :

    status_flag

    flag_meanings :

    good bad

    flag_values :

    [0 1]

    valid_min :

    0

    valid_max :

    1

    comment :

    Quality flag for significant wave height from KaRIn.

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    sig0_karin_qual

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    quality flag for sigma0 from KaRIn.

    standard_name :

    status_flag

    flag_meanings :

    good bad

    flag_values :

    [0 1]

    valid_min :

    0

    valid_max :

    1

    comment :

    Quality flag for sigma0 from KaRIn.

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    num_pt_avg

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    number of samples averaged

    units :

    1

    valid_min :

    0

    valid_max :

    289

    comment :

    Number of native unsmoothed, beam-combined KaRIn samples averaged.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    swh_model

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    significant wave height from wave model

    standard_name :

    sea_surface_wave_significant_height

    source :

    European Centre for Medium-Range Weather Forecasts

    institution :

    ECMWF

    units :

    m

    valid_min :

    0

    valid_max :

    30000

    comment :

    Significant wave height from model.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    mean_wave_direction

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    mean sea surface wave direction

    source :

    Meteo France Wave Model (MF-WAM)

    institution :

    Meteo France

    units :

    degree

    valid_min :

    0

    valid_max :

    36000

    comment :

    Mean sea surface wave direction.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    mean_wave_period_t02

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    sea surface wind wave mean period

    standard_name :

    sea_surface_wave_significant_period

    source :

    Meteo France Wave Model (MF-WAM)

    institution :

    Meteo France

    units :

    s

    valid_min :

    0

    valid_max :

    100

    comment :

    Sea surface wind wave mean period from model spectral density second moment.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    wind_speed_model_u

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    u component of model wind

    standard_name :

    eastward_wind

    source :

    European Centre for Medium-Range Weather Forecasts

    institution :

    ECMWF

    units :

    m/s

    valid_min :

    -30000

    valid_max :

    30000

    comment :

    Eastward component of the atmospheric model wind vector at 10 meters.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    wind_speed_model_v

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    v component of model wind

    standard_name :

    northward_wind

    source :

    European Centre for Medium-Range Weather Forecasts

    institution :

    ECMWF

    units :

    m/s

    valid_min :

    -30000

    valid_max :

    30000

    comment :

    Northward component of the atmospheric model wind vector at 10 meters.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    wind_speed_rad

    (num_lines, num_sides)

    float32

    dask.array<chunksize=(9868, 2), meta=np.ndarray>

    

    long_name :

    wind speed from radiometer

    standard_name :

    wind_speed

    source :

    Advanced Microwave Radiometer

    units :

    m/s

    valid_min :

    0

    valid_max :

    65000

    comment :

    Wind speed from radiometer measurements.

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144, 2) (9868, 2) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    distance_to_coast

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    distance to coast

    source :

    MODIS/GlobCover

    institution :

    European Space Agency

    units :

    m

    valid_min :

    0

    valid_max :

    21000

    comment :

    Approximate distance to the nearest coast point along the Earth surface.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    heading_to_coast

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    heading to coast

    units :

    degrees

    valid_min :

    0

    valid_max :

    35999

    comment :

    Approximate compass heading (0-360 degrees with respect to true north) to the nearest coast point.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    ancillary_surface_classification_flag

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    surface classification

    standard_name :

    status_flag

    source :

    MODIS/GlobCover

    institution :

    European Space Agency

    flag_meanings :

    open_ocean land continental_water aquatic_vegetation continental_ice_snow floating_ice salted_basin

    flag_values :

    [0 1 2 3 4 5 6]

    valid_min :

    0

    valid_max :

    6

    comment :

    7-state surface type classification computed from a mask built with MODIS and GlobCover data.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    dynamic_ice_flag

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    dynamic ice flag

    standard_name :

    status_flag

    source :

    EUMETSAT Ocean and Sea Ice Satellite Applications Facility

    institution :

    EUMETSAT

    flag_meanings :

    no_ice probable_ice ice

    flag_values :

    [0 1 2]

    valid_min :

    0

    valid_max :

    2

    comment :

    Dynamic ice flag for the location of the KaRIn measurement.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    rain_flag

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    rain flag

    standard_name :

    status_flag

    flag_meanings :

    no_rain probable_rain rain

    flag_values :

    [0 1 2]

    valid_min :

    0

    valid_max :

    2

    comment :

    Flag indicates that signal is attenuated, probably from rain.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    rad_surface_type_flag

    (num_lines, num_sides)

    float32

    dask.array<chunksize=(9868, 2), meta=np.ndarray>

    

    long_name :

    radiometer surface type flag

    standard_name :

    status_flag

    source :

    Advanced Microwave Radiometer

    flag_meanings :

    open_ocean coastal_ocean land

    flag_values :

    [0 1 2]

    valid_min :

    0

    valid_max :

    2

    comment :

    Flag indicating the validity and type of processing applied to generate the wet troposphere correction (rad_wet_tropo_cor). A value of 0 indicates that open ocean processing is used, a value of 1 indicates coastal processing, and a value of 2 indicates that rad_wet_tropo_cor is invalid due to land contamination.

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144, 2) (9868, 2) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    sc_altitude

    (num_lines)

    float64

    dask.array<chunksize=(9868,), meta=np.ndarray>

    

    long_name :

    altitude of KMSF origin

    standard_name :

    height_above_reference_ellipsoid

    units :

    m

    valid_min :

    0

    valid_max :

    2000000000

    comment :

    Altitude of the KMSF origin.

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144,) (9868,) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    orbit_alt_rate

    (num_lines)

    float32

    dask.array<chunksize=(9868,), meta=np.ndarray>

    

    long_name :

    orbital altitude rate with respect to mean sea surface

    units :

    m/s

    valid_min :

    -3500

    valid_max :

    3500

    comment :

    Orbital altitude rate with respect to the mean sea surface.

    Array Chunk Bytes 1.09 MiB 38.55 kiB Shape (286144,) (9868,) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    cross_track_angle

    (num_lines)

    float64

    dask.array<chunksize=(9868,), meta=np.ndarray>

    

    long_name :

    cross-track angle from true north

    units :

    degrees

    valid_min :

    0

    valid_max :

    359999999

    comment :

    Angle with respect to true north of the cross-track direction to the right of the spacecraft velocity vector.

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144,) (9868,) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    sc_roll

    (num_lines)

    float64

    dask.array<chunksize=(9868,), meta=np.ndarray>

    

    long_name :

    roll of the spacecraft

    standard_name :

    platform_roll_angle

    units :

    degrees

    valid_min :

    -1799999

    valid_max :

    1800000

    comment :

    KMSF attitude roll angle; positive values move the +y antenna down.

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144,) (9868,) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    sc_pitch

    (num_lines)

    float64

    dask.array<chunksize=(9868,), meta=np.ndarray>

    

    long_name :

    pitch of the spacecraft

    standard_name :

    platform_pitch_angle

    units :

    degrees

    valid_min :

    -1799999

    valid_max :

    1800000

    comment :

    KMSF attitude pitch angle; positive values move the KMSF +x axis up.

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144,) (9868,) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    sc_yaw

    (num_lines)

    float64

    dask.array<chunksize=(9868,), meta=np.ndarray>

    

    long_name :

    yaw of the spacecraft

    standard_name :

    platform_yaw_angle

    units :

    degrees

    valid_min :

    -1799999

    valid_max :

    1800000

    comment :

    KMSF attitude yaw angle relative to the nadir track. The yaw angle is a right-handed rotation about the nadir (downward) direction. A yaw value of 0 deg indicates that the KMSF +x axis is aligned with the horizontal component of the Earth-relative velocity vector. A yaw value of 180 deg indicates that the spacecraft is in a yaw-flipped state, with the KMSF -x axis aligned with the horizontal component of the Earth-relative velocity vector.

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144,) (9868,) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    velocity_heading

    (num_lines)

    float64

    dask.array<chunksize=(9868,), meta=np.ndarray>

    

    long_name :

    heading of the spacecraft Earth-relative velocity vector

    units :

    degrees

    valid_min :

    0

    valid_max :

    359999999

    comment :

    Angle with respect to true north of the horizontal component of the spacecraft Earth-relative velocity vector. A value of 90 deg indicates that the spacecraft velocity vector pointed due east. Values between 0 and 90 deg indicate that the velocity vector has a northward component, and values between 90 and 180 deg indicate that the velocity vector has a southward component.

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144,) (9868,) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    orbit_qual

    (num_lines)

    float32

    dask.array<chunksize=(9868,), meta=np.ndarray>

    

    long_name :

    orbit quality flag

    standard_name :

    status_flag

    valid_min :

    0

    valid_max :

    1

    comment :

    Orbit quality flag.

    Array Chunk Bytes 1.09 MiB 38.55 kiB Shape (286144,) (9868,) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    latitude_avg_ssh

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    weighted average latitude of samples used to compute SSH

    standard_name :

    latitude

    units :

    degrees_north

    valid_min :

    -80000000

    valid_max :

    80000000

    comment :

    Latitude of measurement [-80,80]. Positive latitude is North latitude, negative latitude is South latitude. This value may be biased away from a nominal grid location if some of the native, unsmoothed samples were discarded during processing.

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    longitude_avg_ssh

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    weighted average longitude of samples used to compute SSH

    standard_name :

    longitude

    units :

    degrees_east

    valid_min :

    0

    valid_max :

    359999999

    comment :

    Longitude of measurement. East longitude relative to Greenwich meridian. This value may be biased away from a nominal grid location if some of the native, unsmoothed samples were discarded during processing.

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    cross_track_distance

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    cross track distance

    units :

    m

    valid_min :

    -75000.0

    valid_max :

    75000.0

    comment :

    Distance of sample from nadir. Negative values indicate the left side of the swath, and positive values indicate the right side of the swath.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    x_factor

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    radiometric calibration X factor as a composite value for the X factors of the +y and -y channels

    units :

    1

    valid_min :

    0.0

    valid_max :

    1e+20

    comment :

    Radiometric calibration X factor as a linear power ratio.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    sig0_cor_atmos_model

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    two-way atmospheric correction to sigma0 from model

    source :

    European Centre for Medium-Range Weather Forecasts

    institution :

    ECMWF

    units :

    1

    valid_min :

    1.0

    valid_max :

    10.0

    comment :

    Atmospheric correction to sigma0 from weather model data as a linear power multiplier (not decibels). sig0_cor_atmos_model is already applied in computing sig0_karin_2.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    sig0_cor_atmos_rad

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    two-way atmospheric correction to sigma0 from radiometer data

    source :

    Advanced Microwave Radiometer

    units :

    1

    valid_min :

    1.0

    valid_max :

    10.0

    comment :

    Atmospheric correction to sigma0 from radiometer data as a linear power multiplier (not decibels). sig0_cor_atmos_rad is already applied in computing sig0_karin.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    doppler_centroid

    (num_lines, num_sides)

    float32

    dask.array<chunksize=(9868, 2), meta=np.ndarray>

    

    long_name :

    doppler centroid estimated by KaRIn

    units :

    1/s

    valid_min :

    -30000

    valid_max :

    30000

    comment :

    Doppler centroid (in hertz or cycles per second) estimated by KaRIn.

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144, 2) (9868, 2) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    phase_bias_ref_surface

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    height of reference surface used for phase bias calculation

    units :

    m

    valid_min :

    -15000000

    valid_max :

    150000000

    comment :

    Height (relative to the reference ellipsoid) of the reference surface used for phase bias calculation during L1B processing.

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    obp_ref_surface

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    height of reference surface used by on-board-processor

    units :

    m

    valid_min :

    -15000000

    valid_max :

    150000000

    comment :

    Height (relative to the reference ellipsoid) of the reference surface used by the KaRIn on-board processor.

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    rad_tmb_187

    (num_lines, num_sides)

    float32

    dask.array<chunksize=(9868, 2), meta=np.ndarray>

    

    long_name :

    radiometer main beam brightness temperature at 18.7 GHz

    standard_name :

    toa_brightness_temperature

    source :

    Advanced Microwave Radiometer

    units :

    K

    valid_min :

    13000

    valid_max :

    25000

    comment :

    Main beam brightness temperature measurement at 18.7 GHz. Value is unsmoothed (along-track averaging has not been performed).

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144, 2) (9868, 2) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    rad_tmb_238

    (num_lines, num_sides)

    float32

    dask.array<chunksize=(9868, 2), meta=np.ndarray>

    

    long_name :

    radiometer main beam brightness temperature at 23.8 GHz

    standard_name :

    toa_brightness_temperature

    source :

    Advanced Microwave Radiometer

    units :

    K

    valid_min :

    13000

    valid_max :

    25000

    comment :

    Main beam brightness temperature measurement at 23.8 GHz. Value is unsmoothed (along-track averaging has not been performed).

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144, 2) (9868, 2) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    rad_tmb_340

    (num_lines, num_sides)

    float32

    dask.array<chunksize=(9868, 2), meta=np.ndarray>

    

    long_name :

    radiometer main beam brightness temperature at 34.0 GHz

    standard_name :

    toa_brightness_temperature

    source :

    Advanced Microwave Radiometer

    units :

    K

    valid_min :

    15000

    valid_max :

    28000

    comment :

    Main beam brightness temperature measurement at 34.0 GHz. Value is unsmoothed (along-track averaging has not been performed).

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144, 2) (9868, 2) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    rad_water_vapor

    (num_lines, num_sides)

    float32

    dask.array<chunksize=(9868, 2), meta=np.ndarray>

    

    long_name :

    water vapor content from radiometer

    standard_name :

    atmosphere_water_vapor_content

    source :

    Advanced Microwave Radiometer

    units :

    kg/m^2

    valid_min :

    0

    valid_max :

    15000

    comment :

    Integrated water vapor content from radiometer measurements.

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144, 2) (9868, 2) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    rad_cloud_liquid_water

    (num_lines, num_sides)

    float32

    dask.array<chunksize=(9868, 2), meta=np.ndarray>

    

    long_name :

    liquid water content from radiometer

    standard_name :

    atmosphere_cloud_liquid_water_content

    source :

    Advanced Microwave Radiometer

    units :

    kg/m^2

    valid_min :

    0

    valid_max :

    2000

    comment :

    Integrated cloud liquid water content from radiometer measurements.

    Array Chunk Bytes 2.18 MiB 77.09 kiB Shape (286144, 2) (9868, 2) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    mean_sea_surface_cnescls

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    mean sea surface height (CNES/CLS)

    source :

    CNES_CLS_15

    institution :

    CNES/CLS

    units :

    m

    valid_min :

    -1500000

    valid_max :

    1500000

    comment :

    Mean sea surface height above the reference ellipsoid. The value is referenced to the mean tide system, i.e. includes the permanent tide (zero frequency).

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    mean_sea_surface_cnescls_uncert

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    mean sea surface height accuracy (CNES/CLS)

    source :

    CNES_CLS_15

    institution :

    CNES/CLS

    units :

    m

    valid_min :

    0

    valid_max :

    10000

    comment :

    Accuracy of the mean sea surface height (mean_sea_surface_cnescls).

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    mean_sea_surface_dtu

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    mean sea surface height (DTU)

    source :

    DTU18

    institution :

    DTU

    units :

    m

    valid_min :

    -1500000

    valid_max :

    1500000

    comment :

    Mean sea surface height above the reference ellipsoid. The value is referenced to the mean tide system, i.e. includes the permanent tide (zero frequency).

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    mean_sea_surface_dtu_uncert

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    mean sea surface height accuracy (DTU)

    source :

    DTU18

    institution :

    DTU

    units :

    m

    valid_min :

    0

    valid_max :

    10000

    comment :

    Accuracy of the mean sea surface height (mean_sea_surface_dtu)

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    geoid

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    geoid height

    standard_name :

    geoid_height_above_reference_ellipsoid

    source :

    EGM2008 (Pavlis et al., 2012)

    units :

    m

    valid_min :

    -1500000

    valid_max :

    1500000

    comment :

    Geoid height above the reference ellipsoid with a correction to refer the value to the mean tide system, i.e. includes the permanent tide (zero frequency).

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    mean_dynamic_topography

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    mean dynamic topography

    source :

    CNES_CLS_18

    institution :

    CNES/CLS

    units :

    m

    valid_min :

    -30000

    valid_max :

    30000

    comment :

    Mean dynamic topography above the geoid.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    mean_dynamic_topography_uncert

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    mean dynamic topography accuracy

    source :

    CNES_CLS_18

    institution :

    CNES/CLS

    units :

    m

    valid_min :

    0

    valid_max :

    10000

    comment :

    Accuracy of the mean dynamic topography.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    depth_or_elevation

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    ocean depth or land elevation

    source :

    Altimeter Corrected Elevations, version 2

    institution :

    European Space Agency

    units :

    m

    valid_min :

    -12000

    valid_max :

    10000

    comment :

    Ocean depth or land elevation above reference ellipsoid. Ocean depth (bathymetry) is given as negative values, and land elevation positive values.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    solid_earth_tide

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    solid Earth tide height

    source :

    Cartwright and Taylor (1971) and Cartwright and Edden (1973)

    units :

    m

    valid_min :

    -10000

    valid_max :

    10000

    comment :

    Solid-Earth (body) tide height. The zero-frequency permanent tide component is not included.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    ocean_tide_fes

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    geocentric ocean tide height (FES)

    source :

    FES2014b (Carrere et al., 2016)

    institution :

    LEGOS/CNES

    units :

    m

    valid_min :

    -300000

    valid_max :

    300000

    comment :

    Geocentric ocean tide height. Includes the sum total of the ocean tide, the corresponding load tide (load_tide_fes) and equilibrium long-period ocean tide height (ocean_tide_eq).

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    ocean_tide_got

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    geocentric ocean tide height (GOT)

    source :

    GOT4.10c (Ray, 2013)

    institution :

    GSFC

    units :

    m

    valid_min :

    -300000

    valid_max :

    300000

    comment :

    Geocentric ocean tide height. Includes the sum total of the ocean tide, the corresponding load tide (load_tide_got) and equilibrium long-period ocean tide height (ocean_tide_eq).

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    load_tide_fes

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    geocentric load tide height (FES)

    source :

    FES2014b (Carrere et al., 2016)

    institution :

    LEGOS/CNES

    units :

    m

    valid_min :

    -2000

    valid_max :

    2000

    comment :

    Geocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value has already been added to the corresponding ocean tide height value (ocean_tide_fes).

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    load_tide_got

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    geocentric load tide height (GOT)

    source :

    GOT4.10c (Ray, 2013)

    institution :

    GSFC

    units :

    m

    valid_min :

    -2000

    valid_max :

    2000

    comment :

    Geocentric load tide height. The effect of the ocean tide loading of the Earth's crust. This value has already been added to the corresponding ocean tide height value (ocean_tide_got).

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    ocean_tide_eq

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    equilibrium long-period ocean tide height

    units :

    m

    valid_min :

    -2000

    valid_max :

    2000

    comment :

    Equilibrium long-period ocean tide height. This value has already been added to the corresponding ocean tide height values (ocean_tide_fes and ocean_tide_got).

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    ocean_tide_non_eq

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    non-equilibrium long-period ocean tide height

    source :

    FES2014b (Carrere et al., 2016)

    institution :

    LEGOS/CNES

    units :

    m

    valid_min :

    -2000

    valid_max :

    2000

    comment :

    Non-equilibrium long-period ocean tide height. This value is reported as a relative displacement with repsect to ocean_tide_eq. This value can be added to ocean_tide_eq, ocean_tide_fes, or ocean_tide_got, or subtracted from ssha_karin and ssha_karin_2, to account for the total long-period ocean tides from equilibrium and non-equilibrium contributions.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    internal_tide_hret

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    coherent internal tide (HRET)

    source :

    Zaron (2019)

    units :

    m

    valid_min :

    -2000

    valid_max :

    2000

    comment :

    Coherent internal ocean tide. This value is subtracted from the ssh_karin and ssh_karin_2 to compute ssha_karin and ssha_karin_2, respectively.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    internal_tide_sol2

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    coherent internal tide (Model 2)

    source :

    TBD

    units :

    m

    valid_min :

    -2000

    valid_max :

    2000

    comment :

    Coherent internal tide.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    pole_tide

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    geocentric pole tide height

    source :

    Wahr (1985) and Desai et al. (2015)

    units :

    m

    valid_min :

    -2000

    valid_max :

    2000

    comment :

    Geocentric pole tide height. The total of the contribution from the solid-Earth (body) pole tide height, the ocean pole tide height, and the load pole tide height (i.e., the effect of the ocean pole tide loading of the Earth's crust).

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    dac

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    dynamic atmospheric correction

    source :

    MOG2D

    institution :

    LEGOS/CNES/CLS

    units :

    m

    valid_min :

    -12000

    valid_max :

    12000

    comment :

    Model estimate of the effect on sea surface topography due to high frequency air pressure and wind effects and the low-frequency height from inverted barometer effect (inv_bar_cor). This value is subtracted from the ssh_karin and ssh_karin_2 to compute ssha_karin and ssha_karin_2, respectively. Use only one of inv_bar_cor and dac.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    inv_bar_cor

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    static inverse barometer effect on sea surface height

    units :

    m

    valid_min :

    -2000

    valid_max :

    2000

    comment :

    Estimate of static effect of atmospheric pressure on sea surface height. Above average pressure lowers sea surface height. Computed by interpolating ECMWF pressure fields in space and time. The value is included in dac. To apply, add dac to ssha_karin and ssha_karin_2 and subtract inv_bar_cor.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    model_dry_tropo_cor

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    dry troposphere vertical correction

    source :

    European Centre for Medium-Range Weather Forecasts

    institution :

    ECMWF

    units :

    m

    valid_min :

    -30000

    valid_max :

    -15000

    comment :

    Equivalent vertical correction due to dry troposphere delay. The reported sea surface height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported sea surface height results in the uncorrected sea surface height.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    model_wet_tropo_cor

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    wet troposphere vertical correction from weather model data

    source :

    European Centre for Medium-Range Weather Forecasts

    institution :

    ECMWF

    units :

    m

    valid_min :

    -10000

    valid_max :

    0

    comment :

    Equivalent vertical correction due to wet troposphere delay from weather model data. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported sea surface height (ssh_karin_2) results in the uncorrected sea surface height.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    rad_wet_tropo_cor

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    wet troposphere vertical correction from radiometer data

    source :

    Advanced Microwave Radiometer

    units :

    m

    valid_min :

    -10000

    valid_max :

    0

    comment :

    Equivalent vertical correction due to wet troposphere delay from radiometer measurements. The reported pixel height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported sea surface height (ssh_karin) results in the uncorrected sea surface height.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    iono_cor_gim_ka

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    ionosphere vertical correction

    source :

    Global Ionosphere Maps

    institution :

    JPL

    units :

    m

    valid_min :

    -5000

    valid_max :

    0

    comment :

    Equivalent vertical correction due to ionosphere delay. The reported sea surface height, latitude and longitude are computed after adding negative media corrections to uncorrected range along slant-range paths, accounting for the differential delay between the two KaRIn antennas. The equivalent vertical correction is computed by applying obliquity factors to the slant-path correction. Adding the reported correction to the reported sea surface height results in the uncorrected sea surface height.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    height_cor_xover

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    height correction from KaRIn crossovers

    units :

    m

    valid_min :

    -100000

    valid_max :

    100000

    comment :

    Height correction from KaRIn crossover calibration. To apply this correction the value of height_cor_xover should be added to the value of ssh_karin, ssh_karin_2, ssha_karin, and ssha_karin_2.

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    correction_flag

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    quality flag for corrections

    standard_name :

    status_flag

    flag_meanings :

    good bad

    flag_values :

    [0 1]

    valid_min :

    0

    valid_max :

    1

    comment :

    Quality flag for corrections.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    rain_rate

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    rain rate from weather model

    source :

    European Centre for Medium-Range Weather Forecasts

    institution :

    ECMWF

    units :

    mm/hr

    valid_min :

    0

    valid_max :

    200

    comment :

    Rain rate from weather model.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    ice_conc

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    concentration of sea ice

    standard_name :

    sea_ice_area_fraction

    source :

    EUMETSAT Ocean and Sea Ice Satellite Applications Facility

    institution :

    EUMETSAT

    units :

    %

    valid_min :

    0

    valid_max :

    10000

    comment :

    Concentration of sea ice from model.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    sea_state_bias_cor

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    sea state bias correction to height

    source :

    TBD

    units :

    m

    valid_min :

    -6000

    valid_max :

    0

    comment :

    Sea state bias correction to ssh_karin. Adding the reported correction to the reported sea surface height results in the uncorrected sea surface height. The wind_speed_karin value is used to compute this quantity.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    sea_state_bias_cor_2

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    sea state bias correction to height

    source :

    TBD

    units :

    m

    valid_min :

    -6000

    valid_max :

    0

    comment :

    Sea state bias correction to ssh_karin_2. Adding the reported correction to the reported sea surface height results in the uncorrected sea surface height. The wind_speed_karin_2 value is used to compute this quantity.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    swh_sea_state_bias

    (num_lines, num_pixels)

    float32

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    SWH used in sea state bias correction

    units :

    m

    valid_min :

    0

    valid_max :

    25000

    comment :

    Significant wave height used in sea state bias correction.

    Array Chunk Bytes 77.50 MiB 2.67 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float32 numpy.ndarray

    simulated_true_ssh_karin

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    sea surface height

    standard_name :

    sea surface height above reference ellipsoid

    units :

    m

    valid_min :

    -15000000

    valid_max :

    150000000

    comment :

    Height of the sea surface free of measurement errors.

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    simulated_error_karin

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    KaRIn error

    units :

    m

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    simulated_error_timing

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    Timing error

    units :

    m

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    simulated_error_baseline_dilation

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    Error due to baseline mast dilation

    units :

    m

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    simulated_error_roll

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    Error due to roll

    units :

    m

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    simulated_error_phase

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    Error due to phase

    units :

    m

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

    simulated_error_orbital

    (num_lines, num_pixels)

    float64

    dask.array<chunksize=(9868, 71), meta=np.ndarray>

    

    long_name :

    Error due to orbital perturbations

    units :

    m

    Array Chunk Bytes 155.00 MiB 5.35 MiB Shape (286144, 71) (9868, 71) Dask graph 29 chunks in 59 graph layers Data type float64 numpy.ndarray

• Indexes: (0)
• Attributes: (32)

  Conventions :

  CF-1.7

  title :

  Level 2 Low Rate Sea Surface Height Data Product - Expert SSH with Wind and Wave

  institution :

  CNES/JPL

  source :

  Simulate product

  history :

  2021-09-10 10:37:14Z : Creation

  platform :

  SWOT

  product_version :

  1.1.0.dev33

  references :

  Gaultier, L., C. Ubelmann, and L.-L. Fu, 2016: The Challenge of Using Future SWOT Data for Oceanic Field Reconstruction. J. Atmos. Oceanic Technol., 33, 119-126, doi:10.1175/jtech-d-15-0160.1. http://dx.doi.org/10.1175/JTECH-D-15-0160.1.

  reference_document :

  D-56407_SWOT_Product_Description_L2_LR_SSH

  contact :

  CNES aviso@altimetry.fr, JPL podaac@podaac.jpl.nasa.gov

  cycle_number :

  387

  pass_number :

  1

  equator_time :

  2015-05-01T00:04:28.200105Z

  equator_longitude :

  73.87996988322527

  time_coverage_start :

  2015-04-30T23:38:53.183986Z

  time_coverage_end :

  2015-05-01T00:29:58.780239Z

  geospatial_lon_min :

  0.0004904718944836875

  geospatial_lon_max :

  359.9988969843575

  geospatial_lat_min :

  -78.29252416681717

  geospatial_lat_max :

  78.2921281674593

  left_first_longitude :

  350.2735935134039

  left_first_latitude :

  -77.03347507655957

  left_last_longitude :

  157.4129761538219

  left_last_latitude :

  78.2921281674593

  right_first_longitude :

  350.28137873490164

  right_first_latitude :

  -78.29252416681717

  right_last_longitude :

  157.4108182844071

  right_last_latitude :

  77.03307806541929

  wavelength :

  0.008385803020979

  orbit_solution :

  POE

  ellipsoid_semi_major_axis :

  6378137.0

  ellipsoid_flattening :

  0.003352810664781205

#mapping the dynamic ice flag variable
plt.figure(figsize=(21, 12))
ax = plt.axes(projection=ccrs.PlateCarree())
ax.set_global()
ds.dynamic_ice_flag.plot.pcolormesh(
 ax=ax, transform=ccrs.PlateCarree(), x="longitude", y="latitude", add_colorbar=True
)
ax.coastlines()

<cartopy.mpl.feature_artist.FeatureArtist at 0x7f21ef868c40>